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首页> 外文会议>Conference on Physiology and Function: Methods, Systems, and Applications Feb 16-18, 2003 San Diego, California, USA >Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis
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Robust ordering of independent components in functional magnetic resonance imaging time series data using canonical correlation analysis

机译：使用规范相关分析的功能磁共振成像时间序列数据中独立成分的稳健排序

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摘要

The application of independent components analysis (ICA) to functional magnetic resonance imaging data has been proven useful to decompose the signal in terms of its basic sources. The main advantage is that ICA requires no prior assumption about the neuronal activity or the noise structure, which are usually unknown in fMRI. This enables the detection of true activation components free of random and physiological noise. Hence, this technique is superior to other techniques such as subspace modeling or canonical correlation analysis, which have underlying assumptions about the signal components. Nevertheless, this technique suffers from a fundamental limitation of not providing a consistent ordering of the signal components as a result of the whitening step involved in ICA. This mandates human intervention to pick out the relevant activation components from the outcome of ICA, which poses a significant obstacle to the practicality of this technique. In this work, a simple yet robust technique is proposed for ranking the resultant independent components. This technique adds a second step to ICA based on canonical correlation analysis and the prior information about the activation paradigm. This enables the proposed technique to provide a consistent and reproducible ordering of independent components. The proposed technique was applied to real event-related functional magnetic resonance imaging data and the results confirm the practicality and robustness of the proposed method.
机译：事实证明，将独立分量分析（ICA）应用于功能性磁共振成像数据有助于根据信号的基本来源分解信号。主要优点是ICA无需事先假设神经元活动或噪声结构，这在fMRI中通常是未知的。这使得能够检测没有随机和生理噪声的真正的激活成分。因此，该技术优于其他技术，例如子空间建模或规范相关分析，这些技术对信号分量具有基本假设。然而，由于ICA中涉及的白化步骤，该技术受到根本的限制，即不能提供一致的信号分量排序。这要求人工干预从ICA的结果中挑选出相关的激活成分，这对该技术的实用性构成了重大障碍。在这项工作中，提出了一种简单而健壮的技术来对所得独立分量进行排序。这项技术基于规范的相关性分析和有关激活范例的先验信息向ICA添加了第二步。这使得所提出的技术能够提供独立组件的一致且可再现的排序。将该技术应用于与真实事件相关的功能磁共振成像数据，结果证实了该方法的实用性和鲁棒性。

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